The September 2007 Bear Market in NASA Temperature "Pasts"

Since August 1, 2007, NASA has had 3 substantially different online versions of their 1221 USHCN stations (1221 in total.) The third and most recent version was slipped in without any announcement or notice in the last few days – subsequent to their code being placed online on Sept 7, 2007. (I can vouch for this as I completed a scrape of the dset=1 dataset in the early afternoon of Sept 7.)

We’ve been following the progress of the Detroit Lakes MN station and it’s instructive to follow the ups and downs of its history through these spasms. One is used to unpredictability in futures markets (I worked in the copper business in the 1970s and learned their vagaries first hand). But it’s quite unexpected to see similar volatility in the temperature “pasts”.

For example, the Oct 1931 value (GISS dset0 and dset1 – both are equal) for Detroit Lakes began August 2007 at 8.2 deg C; there was a short bull market in August with an increase to 9.1 deg C for a few weeks, but its value was hit by the September bear market and is now only 8.5 deg C. The Nov 1931 temperature went up by 0.8 deg (from -0.9 deg C to -0.1 deg C) in the August bull market, but went back down the full amount of 0.8 deg in the September bear market. December 1931 went up a full 1.0 deg C in the August bull market (from -7.6 deg C to -6.6 deg C) and has held onto its gains much better in the September bear market, falling back only 0.1 deg C -6.7 deg C.

All records of the August bull market in Detroit Lake pasts have been erased from the NASA website, but I managed to complete my downloads in time and am in a position to try to decode exactly what’s been going on.

First, here is a graphic showing the changes to the Detroit Lakes MN in the August “bull market” as NASA moved quickly to correct the “Y2K” error that I had drawn their attention to. Their patch was essentially a step adjustment at 2000, which had the effect of increasing all earlier values by about 0.8 deg C.

Second, here is a similar graphic showing the changes to Detroit Lakes MN in the September “bear market”. As you can see, Hansen has clawed back most of the gains of the 1930s relative to recent years – perhaps leading eventually to a re-discovery of 1998 as the warmest U.S. year of the 20th century.

Aside from other issues – which we shall get to – we have two crossword puzzles here: where did the data come from? In fact, the precise provenance of the NASA USHCN data has been raised in recent posts – most recently here where I posited the use of a vintage USHCN data set. One of the nice things about climateaudit group is that readers often have good answers. Jerry Brennan suggested that the vintage data at ftp://ftp.ncdc.noaa.gov/pub/data/ushcn/OtherIntermediates/ be consulted. There were two potentially relevant files here hcn_shap_ac_mean.Z and hcn_mmts_mean_data.Z. I examined these files for Detroit Lakes and compared them to the three NASA versions online over the summer and am pretty much able to trace the machinations back to their source.

1) the vintage USHCN data set hcn_shap_ac_mean.Z , as Jerry Brennan surmised, was almost certainly used in the pre-Y2K version and the Y2K-adjusted version;
2) the September bear market at Detroit Lakes MN was precipitated by an unannounced switch to the USHCN data set hcn_doe_mean.Z.

Here are some detailed comparisons. First here is a comparison of the NASA “pre-Y2K” version against the vintage SHAP_AC version. You can see the step at 2000; values – if they were available for plotting – would continue at the upper step. There are slight monthly differences relating to some NASA procedure doing monthly adjustments, but this graphic shows to a moral certainty that the SHAP_AC version was in use pre-Y2K.

The next figure compares the NASA Sept 7 version to the vintage SHAP_AC version. The monthly perturbation introduced by NASA has increased but the two versions are obviously connected – and you can see that the Y2K patch has eliminated the step from using inconsistent versions.

However, the changes introduced in the September bear market changed the relationship as shown below. So what is the provenance of the new data?

It’s not the vintage MMTS version, a comparison to which is shown below:

It’s not the GHCN raw version:

It’s not the GHCN adjusted version:

It’s not the current USHCN raw version:

It’s not the current USHCN TOBS version:

But it looks like the current USHCN “adjusted” version plus the step adjustment (which isn’t needed for this series – something that I observed earlier).

The current USHCN data is located in a file entitled hcn_doe_mean and there is a reference to this file in the source code placed online on Sept 7, 2007. This is a different file than the hcn_shap_ac_mean file that was used prior to Sept 7. Perhaps the change from hcn_doe_mean to hcn_shap_ac_mean is the sort of “simplification” that Hansen had in mind when he said, on the occasion of the code being placed online, that they:

would have preferred to have a week or two to combine these into a simpler more transparent structure, but because of a recent flood of demands for the programs, they are being made available as is. People interested in science may want to wait a week or two for a simplified version.

However, this sort of change should not be introduced in the guise of “simplification”. It’s a substantive change in procedure. Maybe it’s an improvement; maybe it’s not. If Hansen is making changes to “improve” his methodology, users are entitled to know of the change when they’re introduced, not after the fact through reverse engineering.

I have no information on why Hansen is picking this particular time to make unannounced “improvements” to his methodology. However, it seems like a poor time to be doing so, as many people will undoubtedly question the motives of doing so at this particular time – and particularly without any announcement. Of course, it could be an unintentional “accident”, just as the “Y2K” switch in versions was an “accident” – in which case, the timing of a second accident seems particularly inopportune.

98 Comments

but it seems very odd that they would accidentally change provenance

It seems the Canadians share more of the British penchant for understatement than we Yanks do.

In my own life history, when I see flailing like this, it has generally been an indication of panic. In this case it seems that a lot of attention is being paid to minutia and not the overall bigger picture. A clear statement of what data is used and what has been done to it seems to be something that could be whipped out in less than an hour and would go a long way toward defusing things. Instead we see the cloth over the hat ruffling as the rabbit underneath struggles and the magician breaks into a sweat.

Are these substantive changes to all the stations, or was the Detroit Lakes station used because it is the most dramatic example? In other words, will all the other stations show such dramatic changes in past records?

I can understand why Dr. Hanson is acting cavalierly with the data, he sees his life’s work crumbling before his very eyes. Based on the premliminary work on the data sets and methods, the entire premise of this experiment is rapidly devolving into oblivion. In retrospect, using haphazardly sited weather stations intended as a local forcasting tool was probably not such a good idea. The adjustments appear abitrary at best. This coupled with a complete lack of an audit trail and sloppy or nonexistent documentation is reason enough to scrap the whole exercise.

Investigators would not have to resort to piecing the puzzle together if this was a valid and legimate study. Nevertheless, I am anxiously awaiting a definitive conclusion from climate audit.

So is Gavin going to tell us all the steps we need for replication are in Hansen’s prior publications?

Maybe if you hold the papers at the right distance and angle, and focus your eyes on something in the distance, you’ll see one of those embedded image-types of things that tells you what additional steps you need to take between Sept 7 and Sept 10 to get the latest results.

#4. Detroit Lakes MN was used because it’s been a site that’s been discussed as an example since last spring and readers are familiar with it. Eli Rabett/Josh Halpern is the one who particularly drew it to my attention, arguing that the microsite issues at this site “didn’t matter” because NASA/NOAA software could fix bad data.

I was skeptical as to whether their software could adequately deal with microsite problems and investigated this site noticing the Y2K step here.

I did an earlier post in July on the distribution of Y2K adjustments. Detroit Lakes is a relatively large adjustment (about 0.8 K) which is why it was noticeable there. Some Y2K adjustments went the other way, but overall there was a bias of about 0.15 deg C.

Bismarck famously said “Laws are like sausages. It’s better not to see them being made”.

It seems the same could be applied to global temperature reconstructions and I guess the powers that be would rather not know the processes involved as long as the resultant ‘sausages’ taste right to them.

#6. I’m sure that Gavin or Josh Halpern will be along to say that all we ever needed to do was RTFR: I guess in this case, Hansen et al 2001 must be a bit like Nostradamus – holding encrypted clues to change data sets on Sep 11, 2007.

RE: #5 – It is an interesting thing. I (fondly?) remember some of the early discussions I got into over at RC regarding UHI / microsite issues / anthropogenically originated biases. At the time, I can distinctly recall a sort of gloating, appealing to authority tone, whereby it was said, in effect – “Oh that? That is nothing. It’s been adjusted out of the record. Time to move on.”

If so, are the specific dates of the steps (around 1934 and 1952) isolated to this site or do the dates of these steps show up in other sites also? Do the dates correlate with local station phenomena (e.g. station moves, equipment changes)?

The NCDC station history has the following changes noted for Detroit Lakes around the step dates:

Below is what I posted on surfacestations.org Sept 11, concerning availablity of the rural Walhalla SC data from GISS. The newest GISS data no longer shows the early warmest values and the early temperatures from about 1900 to 1910 are shown to be remarkably cooler than they were in the original GISS data. Therefore, it appears that more than just Detroit Lakes has been significantly modified.

To better visualize the differences for my own edification, (not being a mathematician and not having the inclination to make my own spreadsheet charts), I merged the two graphs using photoediting software. The differences between the two versions were quite obvious.

However, now I’ve tried to find the graphs on the GISS web site and all I can find is a different adjusted Walhalla graph. I could not find the raw GISS graph. The new GISS graph has no values higher than 16.5 deg C, whereas the original graphs did. And the differences in the graphs evidenced by merging the latest graph image with the two original ones suggest that a different algorithm is now being used.

Has GISS changed their algorithm, is the raw information no longer available, or is the original information still available at some other web site?”

Considering all the adjustments, revisions and corrections that these data sets have experienced, is it not possible or even probable that the original data sets have been lost beyond recovery? In which case, all the calculations by Hansen and his critics would be a kind of delusional numerology.

It depends on how original you want to get. Historically, most original records
were on paper. Some may have been well preserved, and some may not have been.

Various sets of orginal data were collected by various people, some being
weather historians of sorts, some being members of, for example, national
weather bureaus. Such collectors would often consolidate daily data to
monthly data. After that, the daily data may have been preserved, but not
necessarily.

Eventually, various organizations, for example the US DOE, invested heavily in
digitzing (computerizing) such data, and archiving them. Today, NOAA’s NCDC
has several gigabytes of daily min/max temperature data online, some of it
going back to the 19th century.

So, there are records of original data, but the data sets in which the original
data are stored are not the same data sets in which they were originally recorded.

“However, the above graphs are not of the adjustments, but of differences
between differently adjusted renditions of the adjustments.”

Agreed. I’m wondering if the difference between Sept 10 and Sept 7 (the second plot in the post) was caused by the TOB/other adjustments changing. This seems to be the case. There are large steps in the second plot around 1933 and 1951 that seem to indicate the TOB adjustment of 1933 (TOB changed on 9/1/1933 accoring to station history) and the other adjustment (station move of 10/18/1951) have been modified.

The steps in the plot do seem to correlate to specific changes in the station history record that would have a legitimate reason for an adjustment. It looks like the magnitude of those adjustments have changed.

Many years ago, there lived an emperor who was quite an average fairy tale ruler, with one exception: he cared much about his clothes. One day he heard from two swindlers named Guido and Luigi Farabutto that they could make the finest suit of clothes from the most beautiful cloth. This cloth, they said, also had the special capability that it was invisible to anyone who was either stupid or not fit for his position.

Being a bit nervous about whether he himself would be able to see the cloth, the emperor first sent two of his trusted men to see it. Of course, neither would admit that they could not see the cloth and so praised it. All the townspeople had also heard of the cloth and were interested to learn how stupid their neighbors were.

The emperor then allowed himself to be dressed in the clothes for a procession through town, never admitting that he was too unfit and stupid to see what he was wearing. He was afraid that the other people would think that he was stupid.

Of course, all the townspeople wildly praised the magnificent clothes of the emperor, afraid to admit that they could not see them, until a small child said:

“But he has nothing on!”

This was whispered from person to person until everyone in the crowd was shouting that the emperor had nothing on. The emperor heard it and felt that they were correct, but held his head high and finished the procession.

This story of the little boy puncturing the pretensions of the emperor’s court has parallels from other cultures, categorized as Aarne-Thompson folktale type 1620, although the tale itself has no identified oral sources.[1]

The expressions The Emperor’s new clothes and The Emperor has no clothes are often used with allusion to Andersen’s tale. Most frequently, the metaphor involves a situation wherein the overwhelming (usually unempowered) majority of observers willingly share in a collective ignorance of an obvious fact, despite individually recognizing the absurdity. A similar twentieth-century metaphor is the Elephant in the room. A metaphor of the opposite, in which each individual insists on his or her own perspective in spite of the evidence of others, is shown in the various versions of the Blind Men and an Elephant story.

In one interpretation, the story is also used to express a concept of “truth seen by the eyes of a child”, an idea that truth is often spoken by a person too naïve to understand group pressures to see contrary to the obvious. This is a general theme of “purity within innocence” throughout Andersen’s fables and many similar works of literature.

In another interpretation, the child is not simply a naive person, but precisely a child, as the perspective of children is often unencumbered with the filtering “knowledge” and social conditioning that fills the heads of adults, warping their perspective.

“The Emperor Wears No Clothes” or “The Emperor Has No Clothes” is often used in political and social contexts for any obvious truth denied by the majority despite the evidence of their eyes, especially when proclaimed by the government.

Perhaps a better statement would be that some of the above graphs are of differences
between adjustments, and some are of differences between adjusted temperatures to which
different amounts of those adjustments have been applied.

Yes, that is correct. For example in the United States, some observations and data products were initially recorded on the WBAN Form 10A/B. Although this paper document served as the official and legal record carrying Federal penalties for falsification and so forth, the meteorological observer or flight controller was responsible for communicating the observation report to a weather data center by computer data transmission, telephone to a computer data transmission operator, or other means of communication and summarization. Despite efforts at enforcing quality control (QC), it was possible for errors in the encoding and tranmission of the observation reports to result in differences between the official WBAN Form 10A/B log and those data transmissions.

Although the paper record such as the WBAN Form 10A/B contained entries for all 24 hour, 12 hour, 6 hour, 3 hour, 1 hour, and 10 minute special observations and data elements; the various datasets summarizing the data elements on the original paper typically did not and do not summarize each and every one of the available data elements. Many disregard special observations conducted at ten minute intervals due to special and/or hazardous weather conditions. Other summaries include only the 3 hour and 6 hour observation reports. Consequently, any researcher must understand the provenance of the observational data and the limitations upon the quality of the data due to errors and summarization omissions.

I guess the problem is that the people recording, and much later the people transcribing didn’t see their simple endeavours would get hijacked for political purposes. Talking of which has anyone generated a family tree showing how later publications are based on the same few earlier ones which have been discredited, or are now suspicious, might be interesting to tie this into the IPCC references

Understood, the plots are differences between different versions of monthly temperature datasets, not adjustments.

I was trying to say (not very clearly) that the difference between the Sept 10 dataset and the Sept 7 dataset (shown in the second plot above) has steps in 1933 and 1951. Those dates correspond to a TOBS change and a Station move in the Detroit Lakes station history.

That would seem to imply that the magnitude of the adjustment that was applied for these events have changed for some unknown reason.

Briefly: USHCN comes up with ajustments for such things as TOB, station
moves, and missing data. Those adjustments have remained almost stable
for several years. GISS wanted to use those adjustments except for the
USHCN missing data adjustment, and USHCN prepared a special file to suit
GISS about seven years ago, but in ended with 1999 data.

For the years after 1999 GISS used raw USHCN data, which they got via
GHCN, and last month Steve pointed out to GISS that there were many sharp
jumps between 1999 data and 2000 data. GISS reacted by changing the
adjustments for each station by an amount based on the size of the
adjustments near the end of the period covered by the special file so as
to smooth the jumps.

That was the reason for changing the magnitude of the adjustments early
last month. For each station the change was a relatively constant amount
per year (about 0.82 C or 0.83 C for Detroit Lakes, which is why the dark
line of the first graph is relatively flat), until this week.

This week that change seems not be be anywhere near constant, and I do
not have, nor have I seen, a clear idea of why that is.

Timber, the whole place is coming down. Best to stand outside and far enough away from the house of cards to not get buried. Now we know how it was done, proving it is in the actions of the keepers of the data. When there is no good reason for changes, but the changes are happening anyway, there is a good need for changes, and it’s critical — to keep others from finding out what the truth is.

At most about 1200, i.e. the USHCN records. However, some may have had no
recent USHCN adjustments, so they presumably would not have changed recently,
and many would have had only relatively slight recent USHCN adjustments, so any
changes to them would presumably have been relatively slight.

Elimination of all data points before 1990;
Various adjustments b/ -1.0 and +2.3 to all data points from 1900 through 1909;
(e.g. Jan 1900 (9-9)=4.2 & Jan 1900 (9-13)=5.2, for a delta of -1.0;
Mar 1907 (9-9)=16.5 & Mar 1907 (9-13)=14.2, for a delta of +2.3)
Adjustments from 1919 through 1984 to make winters COLDER and summers WARMER with respect to 9-9 version, ranging
from about -0.1 for dec to feb,
to about -0.2 for sep to nov,
to about -0.3 or -0.4 for mar to may,
to about -0.5 or -0.6 for jun through aug, although there are variations from year to year.
(e.g.: 1940
VERSION,JAN,FEB,MAR,APR,MAY,JUN,JUL,AUG,SEP,OCT,NOV,DEC
RAW(9-9),0.1,6.1,9.0,13.7,18.4,23.6,24.0,24.6,21.6,16.7,10.5,8.3
RAW(9-13),-0.3,5.7,8.8,13.6,18.3,23.7,24.1,24.7,21.2,16.4,10.1,7.8
RAW(9-9)-(9-13),0.4,0.4,0.2,0.1,0.1,-0.1,-0.1,-0.1,0.4,0.3,0.4,0.5)

B. Changes in Walhalla ADJ b/ 9-9 and 9-13-2007 (9-9 minus 9-13):

Elimination of all data points before 1990;
Various adjustments b/ -1.9 and +1.7 to all data points from 1900 through 1909; (e.g. Dec 1901 (9-9)=2.9 & Dec 1901 (9-13)=4.8, for a delta of -1.9; Mar 1909 (9-9)=10.7 & Mar 1909 (9-13)=9.9, for a delta of +1.8)

Adjustments from 1919 through about 1962 to make winters WARMER and summers COLDER with respect to 9-9 version, ranging
from about 0.4 for dec to feb,
to about 0.3 for sep and oct,
to about 0.2 for March,
to about 0.1 for apr & may,
to about -0.1 for jun through aug, although there are variations from year to year. (e.g. 1924:
VERSION,JAN,FEB,MAR,APR,MAY,JUN,JUL,AUG,SEP,OCT,NOV,DEC
ADJ(9-9),3.9,5.2,8.2,14.2,17.5,24.5,24.4,25.5,19.8,15.6,10.4,7.0
ADJ(9-13),4.0,5.3,8.5,14.6,17.9,25.1,24.9,26.0,20.0,15.8,10.6,7.0
ADJ(9-9)-(9-13),-0.1,-0.1,-0.3,-0.4,-0.4,-0.6,-0.5,-0.5,-0.2,-0.2,-0.2,0)

From 1962 to about 2006, the adjustments fit the same seasonal pattern but are diminished in magnitude.

-0.4 on all points from 1889 through 1905 (e.g. Jul 1889 raw=25.5 & jul 1889 adj=25.1)
-0.3 on all points from 1906 through 1933
-0.2 on all points from 1934 through 1962
-0.1 on all points from 1963 through 1990

Calculated adjustments on 9-13-2007 (ADJ – RAW) to make adjusted data COLDER:Elimination of all data points before 1990,
-0.1 on all points from 1900 through 1901,
-0.2 on all points from 1903 through 1944 (e.g. Jul 1905 raw=24.9 & jul 905 adj=25.1),
-0.3 on all points from 1945 through 1987,
-0.2 on all points from 1988 through 1994,
-0.1 on all but 2 points from 1995 through 2001 and
0 on all points from 2001 through 2006

As a result, a given month may have up to 4 different values (e.g. Sep 1955: on 9-9: 23.2 raw and 23 adj and on 9-13: 22.9 raw and 23.2 adj). Also, the adjustment curve has changed significantly, from a linear one to one with a minimum between 1945 and 1987.

It appears that a major data change has taken place perhaps in response to what has been posted on CA. That it has taken place without any announcement is very troubling. It is also hard to believe that this is historical data upon which the results of certain GW models were based. In fact, I would question whether there is any connection between this data and any GW models, since this data is only a few days old at most. (Steve: I can email you my 9-9 Walhalla downloads for your records, if you don’t have them.)

I think Hansen is on to a very clever way to reduce the capital gains taxes paid by individuals. If at the end of each year, you are able to rewrite the price histories of every security bought and sold, one could simply change the purchase price to the sale price and one would never have to pay capital gains tax again.

Then again, if you do this the IRS and SEC would come after you. Come to think of it, maybe sending the IRS and SEC after Hansen might be a step forward.

An anecdote. While what I will share has to do with discrete data (in this case, occurence rates of unwanted events) the principles involved are instructive. A couple years ago, I had to deal with a difficult demand. It was a report on dead-on-arrival (DOA) rate of a product at a customer’s network integration facility. DOA is an embarassing thing, in that it implies that the product has poor infant mortality, has not been tested adquately in the factory, has been mishandled, or all of the above. It is definitely in the category of bad news and negative PR. In my case, what made it difficult was that there was not complete serial number info in either the field data base or the repair data base. Some numbers were missing, some were bogus (typically, someone typed in the model number, or serial number of a component not the whole unit), some were corrupted. That made it essentially impossible to attribute all confirmed DOAs (e.g. ones claimed failed, then confirmed failed in repair) to their actual failure period (a particular month). Some 30% were not attributable at first blush. Working very hard, we improved it a bit – some were obvious typos, some had ancillary info that allowed inference, etc. In the end, we stated a very plainly worded disclaimer describing the issues with data quality, showed the raw claimed rate, the repair rate with only matched S/Ns and an estimated confirmed field rate which used a combination of the process of elimination, guesswork, and the general notion that if something would get returned within 18 months of failing. Imperfect, and honestly described. Now, using this example, let me describe some behaviors, laying out an ethical continuum:

The unethical optimist – Throw out all un matched serial numbers. Report a so called “field DOA rate” based on the “culled” data. Issue no disclaimer. Claim victory. (Enron).

The unethical pessimist – Include only the raw claimed field data. Cry wolf. Create a crisis. (The “Killer AGW” subculture).

The ethical realist – See what I described above in my own case. Try to make lemons into lemonaide. Be forthright about the cases where “data” were dealt with via fudging and guesswork. Dislaim it with “use at your own risk.” (Most honest and practical people do this rountinely)

The ethical purist – refuse to work with the flawed data. End of discussion. (Career limiting move. This backfires and labels the practitioner as being incredibly difficult to work with.)

As you can see, Hansen has clawed back most of the gains of the 1930s relative to recent years – perhaps leading eventually to a re-discovery of 1998 as the warmest U.S. year of the 20th century.

I don’t know. I suppose the downward trend could continue, but more changes would excite too much suspicion. My first impression was the 1930’s heating interrupted with straight shaft of a hockey stick, thereby nullfying the politically valuable theory heating could only be anthropogenic. So the 1930’s values might have been “misread” lower if someone had less than pure intentions. Later, when challenged and possibly concerned someone else will take a look at the old records, the accurate numbers from the old records were substituted in, the so-call “adjustment.” But here a second “adjustment” is made. This “adjustment” does not have the feeling of a “fact check” for “clerical errors” where one corrects the reported to fit the original reports, but an alteration to fit something else. But it’s beyond me, it’s Climate Science after all.

Why doesn’t this whole charade come to a screeching halt as soon as it becomes apparent that the adjustments are on the same order of magnitude as the computed effect? It is so absurd it boggles my mind.

One source of unadjusted raw data are the local newspapers in the various cities where data is produced. Many papers report the previous day’s max and min temperatures and sometimes even the hourly temperatures as provided to them from the local weather office. Therefore, the morgues of many newspapers contain an independent copy of the essentially raw climatological data. Some of these dailies are available online. Most of the others are preserved indefinitely, usually until the building burns down.

For example, NYC City Office and/or Central Park data was available on a daily basis in several printed venues, one in particular would be the NY Times. BTW, I’ve yet to see a single explanation for the USHCNv1 UHI adjusted data compared with the USHCNv1 unadjusted (raw) data a difference of 3.6°C (colder for the UHI) for annual mean temperatures between 1961 and 1990. I’m begining to feel like I’m trapped in an anechoic chamber.

BTW, a 20th century term related to the Emperor’s Clothes fable is “cognitive dissonance” where one’s attitudes don’t reflect one’s beliefs or observations. In either case it’s often difficult to determine whether the observer is legitmately blind or is deliberately lying.

Is anyone tracking all of these stealth revisions? My first hint of GISS hanky-panky was in Crichton’s State of Fear – in the bibliography where he had caught GISS arbitrarily cutting about 20 years off one of their T-graphs to make the trend look scarier. Possible thesis fodder for a Science Historian PhD project, optimistically assuming there are any who would bring a properly critical, skeptical attitude to the project.

In a similar scholarly auditing vein, I have occasionally contemplated a wiki-blog project similar to SurfaceStations.org in which all of the 900+ articles surveyed by Oreskes in the famous 12/3/04 Science paper – “The Scientific Consensus on Climate Change” – would be carefully parsed to see if a), the abstracts are a good representation of the contents, b), the conclusions (and abstracts) are supported by the authors’ own data, and c), how many of the articles surveyed were from the Life Sciences, where AGW is the assumed cause of some effect on the biosphere (ie: Consensus clearly not supported; no AGW data at all). Item C was inspired by a post several years ago in WCR where they looked at a biology paper (in Nature, as I recall), the abstract of which read more like a sermon than a scientific paper.

As Monckton (I think) has noted, Oreskes didn’t study the contents of the articles, just the abstracts and conclusions. I don’t have a big issue with, or interest in, the paper itself (and Peiser’s rather lame effort to refute it), which was simply an effort to see if there was a broad scientific consensus on AGW; Oreskes even stated that the consensus might be wrong. True, she couldn’t resist the occasional moralizing about “duty to our grandchildren”, etc, which really has no place in a scholarly study, but in general, I think the paper did what she intended. My interest is not so much in the vaunted Consensus per se, but in testing how well the data in the exact papers she studied support the authors’ belief in the Consensus. This is a side-bar discussion, I admit, but I think that a study of Belief Bias could be valuable in preventing future manufactured crises.

Oddly, I’ve never been able to locate anything but the short essay mentioned above. When I first heard about it, I just assumed that it was a long-ish, fairly technical, P-R’d paper, with a list of the 900+ articles surveyed, and arranged in tabular form according to the author’s criteria. If I get serious about this, I suppose I could contact Orestes and ask for a list of the articles . . .

. . . trouble is, I’m a busy working Dad, so if anyone else is interested, be my guest.

Statistically speaking, science suffers from an excess of significance. Overeager researchers often tinker too much with the statistical variables of their analysis to coax any meaningful insight from their data sets. “People are messing around with the data to find anything that seems significant, to show they have found something that is new and unusual,” Dr. Ioannidis said.

Is Hansen changing raw data? That’s to say, is he changing the recorded readings from thermometers at weather stations around the world? Or is he adjusting them to correct for errors due to heat island effects, miscalibrations, and the like?

If it’s the former, he should be fired. If it’s the latter, he should be made to explain exactly why he adjusted readings. Personally, I can’t see how an adjusted value can be anything other than an intelligent guess at what the actual value might be.

The guy works for NASA, right? Hansen is entitled to his opinions, but what’s happening now seems to me to scream for NASA senior management to get hold of all this before NASA’s whole credibility gets called into question. Hansen has been called into question over the ‘hockey stick’ and ‘Y2K’, and now he’s being found seemingly rather cavalierly manipulating data – and seemingly raw data according to some posts here. I really don’t understand why NASA aren’t all over this.

I wonder if maybe it’s because Hansen is bigger than NASA now, and is a political player in a much higher stakes game than anything NASA has ever played. Maybe he’s a sort of cuckoo in their nest that they’ve been busily feeding, and now has got to be bigger than his parents, and has taken over the nest, and can adjust/modify data in whatever way he wants with complete impunity.

This thing has to come to a head somewhere. I commend you guys for what you’re doing. You all deserve medals.

Clicking on “raw data”, I downloaded a file called t72312.7.dat and opened it with Notepad++ and resaved it as a .txt file. Then I opened the txt file in a spreadsheet and compared the 9-13-2007 GISS ADJ to t72312.7.dat and took the difference (9-13 minus t72312.7.dat):

9-13-2007 GISS ADJ MINUS t72312.7.dat:

First of all, t72312.7.dat has data from 1889 through 1899 which was apparently removed from the GISS files. In addition, it has data for three months missing from GISS 9-13 ADJ: nov 1906, oct and nov 1907.

For the years 1900 through 1909, the deltas vary but are between +1.8 for jan 1900 and -0.7 for dec 1903, may 1906, aug 1907, jan 1908, with most of the deltas being positive (i.e. GISS 9-13 ADJ being WARMER than t72312.7.dat).

Between 1919 and 1983 the deltas are fairly uniform:

Jan 0.5 to 0.6 until ’44, then 0.6 to 0.7
Feb 0.5 to 0.6 until ’44, then 0.6 to 0.7
Mar 0.7 to 0.8
Apr 0.6 to 0.7
May 0.4 to 0.5 until ’44, then 0.5 to 0.6
Jun 0.5 to 0.6, with 0.7 in ’48 only
Jul 0.4 to 0.5 until ’44, then 0.5 to 0.6
Aug 0.5 to 0.6 until ’44, then 0.6 to 0.7
Sep 0.3 to 0.4 until ’44, then 0.4 to 0.5
Oct 0.5 to 0.6 until ’44, then 0.6 to 0.7
Nov 0.5 to 0.6 until ’44, then 0.6 to 0.7
Dec 0.6 to 0.7 until ’44, then 0.7 to 0.8

Between 1984 and 2006 the deltas are a little larger (i.e. more +) in the winter months (up to +1.1) but decrease in the summer months (down to +0.3) but remain positive.

#48. Hansen is using other people’s raw data. I’ve tried to specify things as HAnsen’s dset=0 or HAnsen’s dset=1 to be precise. For these stations, dset0 and dset1 are identical and are before the Hansen urban adjustment yielding dset2.

In the most recent episode, he’s changed the input version for US stations from what appears to have been the HCN_SHAP_AC 3A series used up to Sept 7, 2007 to the HCN_DOE_MEAN 3A version used in the Sep 10 version. These have been adjusted at NOAA differently.

To the extent that Hansen has described the data that he used in his articles or webpages, the descriptions obviously do not permit both data sets to be used indiscriminately. If a reason arose for changing the input data, then it seems to me that Hansen should have reported the reason and announced the change in data origin.

#50 Steve, I apologize for not being more precise and trying to use your nomenclature. I believe my “RAW” for Walhalla would be dset=0 and my “ADJ” would be dset=2 assuming the “homogeneity” and “urban” adjustments are the same adjustment.

” . . . but whats happening now seems to me to scream for NASA senior management to get hold of all this before NASAs whole credibility gets called into question.”

” . . . maybe its because Hansen is bigger than NASA now . . .”

You got it – even Hansen’s nominal boss, NASA Director Michael Griffin, treads carefully around him. Griffin made a few perfectly reasonable remarks on NPR’s Morning Edition a few months ago, to the effect that maybe we should all just take a deep breath where AGW is concerned, which were immediately attacked by Hansen and hastily retracted. Welcome to Kafka-land.

But that could be changing. See the “Hansen releases the Code” thread, which links to Hansen’s personal blog, in which Hansen unintentionally but clearly reveals that he released the code under pressure – undoubtedly coming from higher up the food chain than Griffin.

#16: Who was responsible for transcribing the old paper records into an electronic form? It would be interesting to see, from a sample of stations, how the paper record compares to the electronic record (has this ever been checked?).

This is just amazing. The further you dig into Hansen and the whole temperature measurement issue, the worse it becomes. First the Y2K bug, then Watts evaluation of temperature stations, now the amazingly morphing raw data. Add in alleged data fabrication for locations like China by Wang and Jones. And this is all for real world, measured data. Proxy data, as in the infamous “hockey stick” are even worse. It’s like the more rocks you turn over the more unpleasant, white, slimy things crawl out. I suspected the measured temperature records to be suspect, but this is just amazing. How in the world has this been let happen? Such sloppiness at best, and overt manipulation at worst?

In another forum, I saw a poster actually complain about people like Steve McIntyre and such as nitpickers who are just dying to find some minor math error to attempt to deny (there’s that word again) the truth. Well, what we’ve seen are far from “minor” errors with little to no effect. There’s an old Heinlein saying to not attribute to malice what’s adequately explained by incompetence, but this whole area looks like its straying well past incompetence as an explanation.

PaddikJ, 46, regarding Oreskes – good points. a) abstracts match contents b) conclusions supported by data and c) how many life sciences-related. But don’t waste your time worrying about it. Note that passive voice (“tested by analyzing”) is used to describe the “survey”, leading me to believe it was done by somebody else, a student(s), for class work maybe? Besides that, the original article had the wrong search terms multiple times. But there are various other issues with that “essay” (looks like Science’s equivalent of an op-ed) that make it rather meaningless to look into the “survey”.

One example of the problems I have with it, it appears she tries to hide the reference to the IPCC in footnote 4, no doubt she cherry picks her quote and what part of TAR it’s from. “Human activities … are modifying the concentration of atmospheric constituents … that absorb or scatter radiant energy. … [M]ost of the observed warming over the last 50 years is likely to have been due to the increase in greenhouse gas concentrations.” The reference points to J. J. McCarthy et al., Eds., Climate Change 2001: Impacts, Adaptation, and Vulnerability Notice page 21.

Notice how she conveniently left off “changes in land cover” and “properties of the surface that” from the quote. (And that it was from the TAR WGII Technical Summary 1.2, such summary “accepted but not approved in detail at the Sixth Session of IPCC Working Group II”)

The main issue though is the reason for this “survey”. She says that AAAS, AMS, AGU, NAS and IPCC make statements and those statements probably reflect the views of the members. But perhaps there are dissenters. I know, let’s go survey scientists. By looking at article abstracts and seeing if there are any dissenters! Hunh?
“…such reports and statements…. might downlplay legitimate dissenting opinions. That hypothesis was tested by analying 928 abstracts…” Now you tell me how looking at abstracts the reports and statements were taken from in the first place is a survey of scientists that might be dissenters. I sure don’t understand it!

She also says “Remarkably, none of the papers disagreed with the consensus position.” Of course they wouldn’t. Those are the types of things the claim of a consensus is taken from! And even then they had to be handpicked it seems. And this wasn’t meant to be replicated. No lists of abstracts, no how they were graded, no criteria of how they were included or excluded. By climatologists only? Anyone involved in climate work? Randomly?

Even all that aside, she didn’t even search for “anthropogenic global warming”.

RE57, JerryB I just downloaded and just got through comparing those three “raw” GISS datasets using a checksum method and found huge differences between the three. The first two changed a little, but the third (most recent) was 25% materially different. Checksums make it easy to spot immediately what the sum of change is.

Either there is some huge new error that was accidentally introduced, or there is something else occurring.

Just to be sure, can you give me the source for each of these files and/or the method used when they were collected and zipped? I just want to be sure I’m looking at the right things and that I am seeing what is in fact raw as opposed to an adjusted dataset.

The difference is something to do with the NOAA station history adjustment – an adjustment that I’ve not probed yet.

But the nerve of them to change their data source. The Sept 7 code refers to the hcn_doe_mean version not the shap_ac version – and to that extent, the code as published is a misrepresentation of how they derived their results.

My criteria ‘b’, “conclusions (and abstracts) are supported by the authors own data”, was, on review, not very well stated. Something like “In addition to conclusions regarding the specific subjects of the papers, are there statements indicating acceptance of the AGW hypothesis which are not supported by the contents of said papers?”, would be closer to the mark.

Putting aside for the moment that at the end of the day, it doesn’t matter how many researchers believe or dis-believe some proposition (re: Crichton’s excellent disparagement of “consensus science”), my immediate interest is in how belief in some over-arching proposition (AGW, in this case) may bias specific research projects. Obviously, I think Oreskes’ search criteria were too narrow, but would concede that she never claimed to do anything but gauge how widespread amoung scientists is the belief in AGW. My interest is in how that belief may or may not be at odds with data gathered and presented in particular research papers. I’ve seen several where the data do not support AGW, but the authors conclude that said data must be anomalous (usually some regional effect), since, of course, AGW is indisputable. I would like to test that on a larger sample, say, Oreskes’, but any randomly generated sampling of papers using a search term like “Anthropic Global Warming” would do. I just think it would be more interesting to use hers, if it’s available, since you could tabulate the new findings along side her original criteria/findings.

I understood your criteria b to basically be that, I was just paraphrasing. My point is that the thing wasn’t meant to be replicated. I think it shouldn’t be replicated. I like your idea to do something on a wider scale, and I’d agree on the face of it, we have a case of “the data must be anomalous becasuse AGW is indisputable” where the conclusion proves itself rather than being researched. Like “If this isn’t a cat, it must be a dog.” or some such.

Papers on all climate-related issues, “for Anthropogenic Global Warming”, from any source where the data in them supports their conclusions would be good to know.

That “survey” was glossing over a few cherry picked abstracts chosen on a faulty premise to prove a point it didn’t and couldn’t prove. (Or probably was obvious on its face once you parse the thing.)

If you want to know what she looked at, I think they went over them at Deltoid quite a bit, probably in the archives (it was a while ago), and she corresponded with some of the folks over there if I remember. So really, some of this has already been done, but not from a neutral point of view it didn’t seem. Like it said, it was a while ago, and following it deeply seemed pointless. As it seemed like the survey itself was meant as a distraction, in an op-ed meant as a diversion. But YYMV. 🙂

You should be able to find the links to various aspects of the subject at Wikipedia (Oreskes, scientfic consensus, global warming dissenters, scientific opinion on climate change, some such like that) I’m sure. It’s probably a perfectly fine place to go to find links to the original stuff they link to so you can actually read for yourself unfiltered. (I’m not happy with the way they’ve phrased this matter to say the least.)

#41, #49 UPDATE I checked the annual average mean temperature for Walhalla from CDIAC. First, go to this page at CDIAC (these are supposed to be USHCN files). Then click on “go to monthly data user interface” at the bottom of the page. In IE6, a new window will open with a map of the US, where you can click on a state, then on a station to get to a page that will build a file for you in .csv format. I downloaded the “mean temperature” for Walhalla and compared it to my 9-9-2007 GISS dset=0 (my “raw”).

All values were in degrees F. There was no data prior to 1900. However, the data discontinuities that existed in all the GISS files (i.e. may, oct, nov & dec 1902;
mar, apr 1903;
nov 1905;
apr, dec 1906;
nov, dec 1907;
mar to dec 1908;
jan, feb 1909;
1910 to 1917;
jan to aug 1918;
and mar 2000) were not present in the CDIAC version, although the CDIAC version only had data through 2005. At first, I thought that the missing data in the GISS versions might have been due to WW1, but the values in the CDIAC version would tend to negate this. In addition, it would not appear that the CDIAC version data for the years missing in the GISS version is a reconstruction due to the difficulty/unlikelihood that monthly versions would have been filled in for such a large gap, but I suppose anything is possible.

After converting CPIAC to Celsius, the deltas for the period 1900 to 1909 varied with no particular pattern from -3.2 for nov 1901 (i.e. 9-9 was 3.2 degrees C COLDER than CPIAC) to +2.5 for dec 1903.

From 1919 through 1984, a familiar pattern emerged, with the 9-9 GISS version 1.2 to 1.3 degrees WARMER in the winter months and 0.2 to 0.3 degrees WARMER in the summer months with the spring and fall having intermediate deltas. From 1985 through 2005, the 9-9 GISS version was 0.8 to 0.9 degrees C WARMER in the winter months and 0.3 to 0.4 degrees C WARMER in the summer months, with the spring and fall months again having intermediate deltas.

In case I wasn’t clear, both the CDIAC and the 9-9 GISS dset=0 files are supposed to be unadjusted for UHI. An version adjusted for UHI is also apparently available from CDIAC.

I attribute the fact that the temps are holding to the fact that people are buying degrees and holding on to them in the hopes of future appreciation. However, if the market keeps declining they will be left with worthless degrees.

The Hockey stick adjustment was just a flash in the pan. It built the base for Y2K.

Since Y2K busted out Congress critters (or their staff) are monitoring this joint in the hopes of collecting further goodies. Editorial writers (Cal Thomas comes to mind – way to go Steve) are eying the joint. Ordinary idiots (like me) have come out of the woodwork to join the fun.

There is nothing right now that a certain political party wouldn’t give – not for proof of no AGW – but for proof of cooking the books. Even lawyers can understand accounting. None of this makes any sense unless the AGWers think they have to act soon before the consensus evaporates. Perhaps in his heart of hearts Hansen fears the solar scientists are right. Of course this paragraph is political speculation of the rankest sort and not to be taken seriously at all.

Maybe the fish has been dead for a while, because it is sure starting to smell.

Regarding the effects of GISS changing their input file for USHCN
stations from an old USHCN file to to a recent one, some background.

There have been several “editions” of USHCN data (I’ll use the word
edition, rather than version, to avoid confusion with NCDC references
to a new version 2 of USHCN). To distinguish among the several editions
I will use the date of the end year of the data contained in each. Thus,
there have been 1994, 1999, 2000, 2002, 2003, and 2006 editions of USHCN.
(The apparent 2005 edition at CDIAC is simply a copy of the 2006 edition
minus the data for the year 2006.)

A principal difference between successive editions is the addition of
data for year(s) since the previous edition. However, other changes
also happen. In particular, SHAP (Station History Adjustment Program)
adjustments change, even though the station histories do not. The USHCN
station history file has not been updated since late 1994.

On average, the net effects of the changes in SHAP adjustments are
relatively small, i.e. less than 0.1 F, but for individual stations they
may be several tenths of a degree F, in some cases more than 1 F, and may
also change sign. (USHCN uses Fahrenheit.)

So the effects of GISS changing their input file for USHCN stations would
depend, in part, on SHAP adjustment changes between their old USHCN file
and the one they currently use.

On reviewing data from 1999, and 2000, USHCN editions, for stations of
which the SHAP adjustments differed between those editions, such as
Lakin, Kansas, and Hammon, Oklohoma, and comparing such data with GISS
files from early August, i.e. prior to the implimentation of the “Y2K
patch”, I must conclude that their old file was a 1999 edition.

Without dumping too much data here, I would like to try to provide some
(very rough), and indirect, indications of changes of SHAP adjustments
between the 1999, and the 2006, USHCN editions.

Following are some station counts and averages of annual USHCN
temperature means adjusted through FILNET of USHCN editions for the
indicated years, and differences (all temperature numbers in degrees F).
Differences of SHAP adjustments would be the main contributors to the
differences of these adjusted temperature means.

OK, a rough translation: in the 2006 edition, most old years appear
slightly cooler than in the 1999 edition. Surprise, surprise.

As mentioned, SHAP adjustments for individual stations may change
considerably between USHCN editions, and in the case of Detroit Lakes,
they did change considerably between the 1999, and the 2006, editions.

#79 JerryB
I wonder if what we are seeing could be a “simple” real time adjustment as it were. Imagine if every so often we were to come up with a “most likely adjustement”. We would typically assume that the present data was more accurate, and more in line with present requirements. We would then use our calibration period to come up with the new offset. Now suppose this is what is being done. Assume in 1999, we were using 1985 to 1995. Then in 2005, we started using 1991 to 2001. Whether the apparent temperature went up because of UHI or GW, in order to come up with our new and improved version, the past would have to be decreased with respect to the present. I wonder if there is a way to tell with the data/procedures if this was done?

#81 I guess I need to look into the details of “mainly” SHAP adjustments versus FILNET. Thanks.

Subtracting TOB from FILNET temperatures (in degrees F by the
way) will give numbers that consist mainly of the SHAP adjustments for
that USHCN edition, using the end year of the included data as the “name”
of each edition.

I am a new reader of your site, so forgive me if what I am saying has been covered before.

Weather station siting

I just returned from a two week trip through the US National Parks travelling through Utah, Wyoming, Idaho and Arizona. Thanks to your excellent efforts to provide better accuracy in weather/climate reporting, I spotted about 15 weather sites during my trip of 3,000 miles. What struck me most was, with the exception of one, all were within 40 feet of the pavement (mostly blacktop). One was in the median of an Interstate highway, within 15 feet of both sides. Most appeared new, or at least the chain link fence appeared bright and shiney. My thought was the proximity to such a heatsink might skew the results. Are you the person tracking these sites? If not, would you give me his name and e-mail address and I can give general locations of some of them.

In the 70’s when there was concern with a possible new ice age, some folks were suggesting powdered coal be dispersed by aircraft to promote snow melt and absorb solar radiation. I also believe I read (possibly in Aviation Week) that the Soviets were experimenting with the concept. If there were ever an area that would benefit (if you look only at agriculture), it would be Siberia. Have you heard of this? Possibly someone with access to Lexis-Nexis can research it for you. It might explain the high anomalous temperatures there.

Yes, it is true, spreading coal dust and other materials to melt the snow and ice has been tried by recent researchers and for many centuries earlier.

Over a thousand years ago, farmers in Asia knew that dark colors absorb the solar energy. So, they spread dark-colored materials such as soil and ashes over snow to promoted melting, and this is how they watered their crops in the springtime. Chinese and Russian researchers have recently tried something similar by sprinkling coal dust onto glaciers, hoping that the melting will provide water to the drought-stricken countries of India, Afghanistan, and Pakistan. However, the experiment proved to be too costly, and they have abandoned the idea.http://www.mvs.usace.army.mil/Shelbyville/Glaciers.htm

RE: #84 – And getting a bit further into “Coast to Coast AM” territory, there was the Russian Woodpecker. There was an undercurrent of suspicion about that system, regarding some attempt to, by tweaking the magnetic field lines and ion distributions in the near polar region, influence the weather. Again, there are credible explanations that the “woodpecker” was actually a typical VLF com system for submarines.

The Russians have been trying for centuries to get people to live in Siberia. They have not been above cooking the books to give people false impressions. They may have done this with temperatures. It may be as simple as mounting the weather station near a conduit for the district heating steam system.

OTH (Over the Horizon) HF (High Frequency meaning “shortwave” as on 3 – 30 MHz) RADAR; unsure of it’s exact purpose or what exactly they could see (pull out of the ‘mud’ so to speak with any received signals they picked up) as the years during which this was in service was quite awhile before easily-implemented DSP techniques … circa ’78 – ’79 at least when I worked at Heathkit (New Product Engineering/Ham Radio/Comm Dept) in Benton Harbor (ST. Joseph actully on Hilltop Road overlooking Lk Michigan) we would crank up a rig on 20 M (14 MHz) and using the electronic CW (Morse code) keyer send back a string of dits and Woody would QSY (shift frequency).

I think Woody was looking for ship, aircraft and even perhaps missile activity; each would have a characteristic Doppler and/or range-rate shift impressed on any returning/reflected RADAR echos from … any where in the ‘skip zone’ of the radio signal. Today, there are still OTH RADARs that can be picked up; I recorded a few minutes on a cassette recorder for analysis of the ‘swept’ or chirped signal for later non-real-time analysis.

“Phased array” antennas would have been used to form/steer the beam towards intended compass bearings to target specific areas of interest; it is tough create a 14 MHz ‘dish’ of any practical use so a series of vertical elements ‘fed’ as requrired accomplishes nearly the same trick (wave reinforcement or cancellation/simple wave physics).

Having lived a good bit of that period, having also endeavored in parallel if not directly in identical ‘fields’ in some cases, underscored with a technical (engineering) background to boot … I like to rely upon my own knowledge base as a ‘check’ on anything else that happens to drift my direction in the way of anecdotes, stories, accounts (call it auditing, if you will, keeping the discussion in line with the purpose of this site); not everything one reads on Wikipedia (or the internet) is authoratative, right true *or* complete.

Lacking here now, in any discussion of the Woodpecker are accounts that would have discussed ‘it’ at the time it was taking place in ham publications e.g. as QST, 73 Magazine; discussions such as DFing the source of the pulses (to provinces of the old USSR).

(As an aside, I imagine an assembled fleet of SAC B-52 bombers would have had quite a signature on ‘the woodpecker’, as did the daily readyness flights the 52’s particiapated in back in those days as part of the nuclear triad that was monitored: missiles, subs, airborn delivery platforms.)

OTH (Over the Horizon) HF (High Frequency meaning shortwave as on 3 – 30 MHz) RADAR; unsure of its exact purpose or what exactly they could see (pull out of the mud so to speak with any received signals they picked up) as the years during which this was in service was quite awhile before easily-implemented DSP techniques

Quite a lot, actually. The original successful OTH early warning radar had a transmit array in Rome, NY, and a receive array in Maine. It was put in place in 1955. From the wiki:

The USAF’s Rome Laboratory had the first US success with their AN/FPS-118 OTH-B. A prototype with a 1 MW transmitter and a separate receiver was installed in Maine, offering coverage over a 60 degree arc between 900 to 3,300 km.

They could detect missile launches, bombers, etc. Quite a useful beast. Now they are used primarily for other things, such as NOAA modeling ocean currents/wind patterns.

Kwinkidentally, I just wrote a proposal for developing orthogonal waveforms for multiple-input, multiple-output (MIMO) radar, particularly for use in OTH radar. The original systems all used the same waveform, generally a frequency modulated continuous wave.

Please; my comment was related to what ‘they’ could see over the continental US or Canada (AS the signal from said Woodpecker was receivable quite easily stateside) given primitive (by today’s standards) technology; the RADAR equation pretty much spells out the paramters to obtain a given signal strength of a certain value before the application of S/N improvement techniques (like pulse compression, alluded to earlier when I cited ‘chirping’).

These replies are meant to be general in nature, relating back to ClimateAudit topics of discussion (I believe any number of us have easy access to such works as Skolnik’s (NOTA BENE: his “RADAR Handbook”) so perhaps we should lay low on further discourse on this subj).

Re #83 on climate alterations .
Science News – 9/1/2007, page 141 Has a brief article about Greenland receiving some soot from Canadian wild fires. However, the amount increased around 1850, when mills and power plants in CXanada and elsewhere in Northern Hemisphere began burning lots of coal.
Industrial soot fell at its greatest rate between 1906 and 1910, causing the darkened snow to absorb about eight times as much solar radiation as it would have if free of soot. The change in energy balance warmed the snow and influenced climate.
More recently, changes in regulations and technology have significantly reduced emissions of industrial soot. However, in the past few decades, Arctic snow has on average absorbed about 40% more solar energy than it did before the Industrial Revolution – see Sept 9 Science.

#97 Roger. Over time, as the darkened snow heats more than the other, won’t the darkened (carbon particles) snow tend to sink more, and become less heat absorbing as it sinks, as it is no longer exposed to the suns rays?

NOAA has deleted the files referred to in this post, the ones that were previously archived in this directoryftp://ftp.ncdc.noaa.gov/pub/data/ushcn/OtherIntermediates/ , and ones that were required to reconcile the former GISS results. If anyone has a copy of the SHAP file, I’d appreciate it.

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